system

Abstract

The system according to this embodiment aims to quickly and efficiently search for documents and videos shared within an organization and provide the necessary information. [Solution] The system according to the embodiment comprises an indexing unit, a reception unit, a suggestion unit, and a provision unit. The indexing unit indexes materials and videos. The reception unit receives user search queries. The suggestion unit suggests appropriate materials and videos based on the search queries received by the reception unit. The provision unit provides the materials and videos suggested by the suggestion unit to the user.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is time-consuming to search for materials and videos shared within an organization, and it is difficult to quickly find the necessary information.

[0005] The system according to the embodiment aims to quickly and efficiently search for materials and videos shared within an organization and provide the necessary information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an indexing unit, a reception unit, a suggestion unit, and a provision unit. The indexing unit indexes materials and videos. The reception unit receives user search queries. The suggestion unit suggests appropriate materials and videos based on the search queries received by the reception unit. The provision unit provides the materials and videos suggested by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and efficiently search for documents and videos shared within an organization and provide the necessary information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An information retrieval system according to an embodiment of the present invention not only facilitates the search for videos and documents shared within an organization, but also automatically finds what is needed. This information retrieval system automatically indexes documents and videos within the organization and suggests appropriate items based on the user's search query. It also implements a recommendation function tailored to work content and job duties to provide users with the most suitable documents and videos. For example, the information retrieval system uses an AI agent to automatically index documents and videos within the organization. In this process, the AI ​​agent analyzes the content of the documents and videos and assigns keywords and tags. Examples include meeting minutes, presentation materials, and training videos. This makes it easier to search for documents and videos. Next, the information retrieval system uses an AI agent to suggest appropriate items in a short time based on the user's search query. When a user enters a search query, the AI ​​agent searches for highly relevant items from the indexed documents and videos. For example, for a search query such as "I want project management documents," it suggests relevant documents and videos. Furthermore, the information retrieval system implements a recommendation function tailored to work content and job duties. The AI ​​agent recommends the most suitable documents and videos based on the user's work content and job duties. For example, the system recommends project management materials to project managers and technical training videos to engineers. Furthermore, the information retrieval system utilizes generative AI to accurately understand the user's search intent. It employs large-scale language models (LLMs) to perform natural language understanding (NLU) and analyze the user's search intent. It also summarizes the content of materials and videos using text generation technology and suggests highly relevant items. For example, if a user searches for "new project plans," the generative AI summarizes and suggests relevant plans. Finally, the information retrieval system learns from past search and browsing history to provide personalized suggestions. The AI ​​agent learns from the user's past search and browsing history and suggests the most suitable materials and videos. For example, a user who has frequently searched for project management materials in the past will be suggested new and relevant materials.This allows the information retrieval system to streamline information retrieval within the organization, reduce work stress, save time, and improve productivity.

[0029] The information retrieval system according to this embodiment comprises an indexing unit, a reception unit, a proposal unit, and a provision unit. The indexing unit indexes documents and videos. The indexing unit analyzes the content of documents and videos and assigns keywords and tags. For example, the indexing unit can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. The indexing unit can also analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, the indexing unit can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from audio data. It can analyze the content of videos using image analysis and extract keywords from image data. The reception unit receives user search queries. The reception unit can receive search queries in the form of, for example, keyword search or natural language search. For example, the reception unit can analyze keywords entered by the user and accept them as search queries. The system can analyze user input using natural language search and accept it as a search query. The suggestion department proposes appropriate materials and videos based on the search queries received by the reception department. For example, the suggestion department can search for and propose highly relevant materials and videos from an indexed database. For example, the suggestion department can propose highly relevant materials and videos based on the user's search query. The suggestion department has a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. For example, the suggestion department can recommend project management materials to project managers and technical training videos to engineers. The suggestion department can analyze the user's search intent using generative AI and propose highly relevant materials and videos. For example, the suggestion department can analyze the user's search intent using generative AI and propose highly relevant materials and videos. The suggestion department can summarize the content of materials and videos using text generation technology and propose highly relevant ones.For example, the suggestion unit can summarize the content of materials and videos using text generation technology and suggest highly relevant ones. The suggestion unit can learn from past search and browsing history and make personalized suggestions. For example, the suggestion unit can learn from a user's past search and browsing history and suggest the most suitable materials and videos for the user. The provision unit provides the materials and videos suggested by the suggestion unit to the user. The provision unit can provide the materials and videos, for example, through a web application or a mobile application. For example, the provision unit can provide materials and videos through a web application. It can provide materials and videos through a mobile application. As a result, the information retrieval system according to the embodiment allows for smooth searching of materials and videos, and enables users to automatically find what they need.

[0030] The indexing unit indexes documents and videos. For example, the indexing unit analyzes the content of documents and videos and assigns keywords and tags. Specifically, it can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. This allows users to easily find related documents and videos when searching later. The indexing unit can also analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, it can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. This allows for efficient understanding of the document content and improves search accuracy. It can analyze the content of videos using audio analysis and extract keywords from the audio data. For example, it can extract important statements and topics from meeting recordings and index them as keywords. It can analyze the content of videos using image analysis and extract keywords from image data. For example, it can extract important charts and text from presentation slides or training video footage and index them as keywords. This allows the indexing unit to efficiently analyze diverse formats of materials and videos, improving search convenience. Furthermore, the indexing unit can store the analysis results in a database and utilize them in conjunction with other departments and systems. For example, the indexed data can be made accessible to the proposal and provision departments, forming a foundation for providing users with the most suitable materials and videos. In this way, the indexing unit can improve the overall performance of the information retrieval system and enhance user convenience.

[0031] The reception desk receives user search queries. The reception desk can accept search queries in various formats, such as keyword searches and natural language searches. Specifically, the reception desk can analyze keywords entered by the user and accept them as search queries. For example, if a user enters "project management," the reception desk can analyze this keyword and process it as a query to search for related documents and videos. Using natural language search, the reception desk can analyze sentences entered by the user and accept them as search queries. For example, if a user enters "I'm looking for the latest training videos on project management," the reception desk can analyze this sentence and convert it into an appropriate search query. This allows users to search using natural language, making information retrieval more intuitive. Furthermore, the reception desk also supports voice input, allowing users to enter search queries by voice. For example, if a user uses a smartphone or microphone and says "Find project management documents," the reception desk can analyze the voice data and process it as a search query. This allows users to search hands-free, improving convenience. The reception desk can learn from the user's search history and past queries to improve the accuracy of search queries. For example, for users who have frequently searched for "project management" in the past, relevant keywords and tags can be suggested preferentially. This allows the reception desk to personalize the user's search experience and provide information more efficiently.

[0032] The suggestion department proposes appropriate materials and videos based on search queries received by the reception department. For example, the suggestion department can search for and propose highly relevant materials and videos from its indexed database. Specifically, it can suggest highly relevant materials and videos based on the user's search query. For example, if a user searches for materials related to "project management," the suggestion department can prioritize suggesting project management-related materials and videos from its indexed database. The suggestion department also features a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. For example, the suggestion department can recommend project management materials to project managers and technical training videos to engineers. This allows users to efficiently obtain the most relevant information for their work. The suggestion department can also utilize generative AI to analyze the user's search intent and propose highly relevant materials and videos. For example, the suggestion department can use generative AI to analyze the user's search intent and propose highly relevant materials and videos. The generative AI analyzes the user's search query, extracts relevant keywords and tags, and proposes the most suitable materials and videos. The suggestion function can summarize the content of documents and videos using text generation technology and suggest highly relevant ones. For example, the suggestion function can summarize the content of documents and videos using text generation technology and suggest highly relevant ones. This allows users to grasp the necessary information in a short amount of time. The suggestion function can learn from past search and browsing history and provide personalized suggestions. For example, the suggestion function can learn from a user's past search and browsing history and suggest the most suitable documents and videos for that user. This allows the suggestion function to provide optimal information tailored to the user's needs and improve the search experience.

[0033] The provisioning department provides users with materials and videos proposed by the proposal department. The provisioning department can provide materials and videos, for example, through web applications or mobile applications. Specifically, the provisioning department can provide materials and videos through web applications. For example, when a user accesses the information retrieval system using a web browser, the provisioning department can display the proposed materials and videos on a web page. Users can view materials and play videos on the web page. Materials and videos can also be provided through mobile applications. For example, when a user accesses the information retrieval system using a smartphone or tablet, the provisioning department can display the proposed materials and videos on the mobile application. Users can view materials and play videos on the mobile application. This allows users to access the information they need, regardless of location or time. Furthermore, the provisioning department can provide a download function for materials and videos. For example, it can allow users to download proposed materials and view them offline. The provisioning department can also provide a sharing function for materials and videos. For example, it can allow users to share proposed materials and videos with other users. This allows users to efficiently obtain the information they need and share it with other users. The service provider can collect user feedback and continuously improve the quality of the materials and videos they provide. For example, users can leave ratings and comments on the materials and videos provided. This allows the service provider to provide the most suitable information to meet user needs and improve the overall quality of the information retrieval system.

[0034] The indexing unit can analyze the content of documents and videos and assign keywords and tags. For example, the indexing unit can analyze the content of documents and videos and assign keywords and tags. For example, the indexing unit can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. In addition, the indexing unit can analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, the indexing unit can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from the audio data. It can analyze the content of videos using image analysis and extract keywords from the image data. This makes it easier to search for documents and videos.

[0035] The recommendation section features a recommendation function that suggests the most suitable materials and videos based on the user's work content and job responsibilities. For example, the recommendation section can recommend project management materials to project managers and technical training videos to engineers. The recommendation section can perform recommendations using algorithms such as collaborative filtering and content-based filtering. For example, collaborative filtering can be used to make recommendations based on materials and videos viewed by other users. Content-based filtering can be used to make recommendations based on the user's past browsing and search history. This allows the recommendation section to provide the user with the most suitable materials and videos.

[0036] The proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. For example, the proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. For example, the proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. Generative AI is implemented using models such as large-scale language models. Generative AI takes the user's search query as input and outputs highly relevant materials and videos. For example, generative AI takes the user's search query as input and outputs highly relevant materials and videos. This makes it possible to suggest appropriate materials and videos based on the user's search intent.

[0037] The proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. For example, the proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. For example, the proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. Text generation technology can be implemented using technologies such as natural language generation or template-based generation. Text generation technology takes the content of documents and videos as input and outputs a summary. For example, text generation technology takes the content of documents and videos as input and outputs a summary. This allows users to quickly obtain the information they need by summarizing and proposing the content of documents and videos.

[0038] The suggestion unit can learn from past search and browsing history to provide personalized suggestions. For example, the suggestion unit can learn from past search and browsing history to provide personalized suggestions. For example, the suggestion unit can learn from a user's past search and browsing history to suggest the most suitable materials and videos for the user. The suggestion unit can use technologies such as user preference analysis and behavioral pattern analysis to provide personalized suggestions. For example, the suggestion unit can analyze user preferences to suggest the most suitable materials and videos for the user. The suggestion unit can analyze user behavior patterns to suggest the most suitable materials and videos for the user. This allows the suggestion unit to provide the most suitable materials and videos for the user.

[0039] The indexing unit can adjust the level of detail in indexing documents and videos based on the importance of the content. For example, the indexing unit can index minutes of important meetings in detail to make them easier to search. The indexing unit can perform simplified indexing on general training videos. The indexing unit can index project progress reports with a moderate level of detail. This improves search accuracy by adjusting the level of detail in indexing based on the importance of the content. Some or all of the above processing in the indexing unit may be performed using AI, for example, or not using AI. For example, the indexing unit can input the content of documents and videos into a generating AI and have the generating AI perform the adjustment of the level of detail in indexing.

[0040] The indexing unit can apply different indexing algorithms depending on the category of the document or video during the indexing process. For example, the indexing unit can apply an indexing algorithm that emphasizes technical terms to technical documents, an indexing algorithm that emphasizes keyword frequency to marketing documents, and an indexing algorithm that takes viewing time into account to training videos. By applying a category-appropriate indexing algorithm, the accuracy of searches is improved. Some or all of the above-described processes in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input category information of documents and videos into a generating AI and have the generating AI execute the application of the indexing algorithm.

[0041] The indexing unit can perform indexing while considering the attribute information of the creators of the materials and videos. For example, if the creator of the materials and videos is an expert, the indexing unit can perform indexing that emphasizes specialized terminology. If the creator of the materials and videos is a beginner, the indexing unit can perform indexing that emphasizes general terminology. If the creator of the materials and videos belongs to a specific industry, the indexing unit can perform indexing that emphasizes industry-specific terminology. This makes it possible to perform more appropriate indexing by considering the attribute information of the creators. Some or all of the above processing in the indexing unit may be performed using AI, for example, or without using AI. For example, the indexing unit can input the attribute information of the creators of the materials and videos into a generating AI and have the generating AI perform the indexing.

[0042] The indexing unit can improve the accuracy of indexing by referring to related literature for documents and videos during the indexing process. For example, the indexing unit can refer to related literature for documents and videos to improve the accuracy of indexing. For example, the indexing unit can refer to related literature for documents and videos to select keywords. The indexing unit can refer to related literature for documents and videos to assign tags. The indexing unit can refer to related literature for documents and videos to adjust the level of detail of the indexing. As a result, the accuracy of indexing is improved by referring to related literature. Some or all of the above processes in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input related literature for documents and videos into a generating AI and have the generating AI perform the indexing accuracy improvement.

[0043] The reception unit can select the optimal reception method when receiving a search query by referring to the user's past search history. For example, the reception unit can automatically display search queries that the user has frequently entered in the past as suggestions. The reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can predict and suggest search queries to be used during a specific time period based on the user's past search history. This makes it possible to receive the optimal search query by referring to past search history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past search history into a generating AI and have the generating AI select the optimal reception method.

[0044] The reception unit can filter search queries based on the user's current work situation. For example, if the user is performing project management tasks, the reception unit can prioritize receiving relevant search queries. If the user is performing technical tasks, the reception unit can prioritize receiving technical search queries. If the user is performing marketing tasks, the reception unit can prioritize receiving marketing-related search queries. This allows for the reception of more appropriate search queries by filtering based on the user's current work situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current work situation into a generating AI and have the generating AI perform the filtering.

[0045] The reception unit can prioritize receiving highly relevant queries by considering the user's geographical location when receiving search queries. For example, the reception unit can prioritize receiving highly relevant queries by considering the user's geographical location when receiving search queries. For example, if the user is in a specific region, the reception unit can prioritize receiving search queries related to that region. If the user is on a business trip, the reception unit can prioritize receiving search queries related to the business trip destination. If the user is at home, the reception unit can prioritize receiving search queries related to home. In this way, it becomes possible to prioritize receiving highly relevant queries by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI execute the process of receiving highly relevant queries.

[0046] The reception unit can analyze the user's social media activity when receiving search queries and accept relevant queries. For example, the reception unit can prioritize receiving search queries related to topics that the user frequently mentions on social media. The reception unit can prioritize receiving search queries related to accounts that the user follows on social media. The reception unit can prioritize receiving search queries related to groups that the user participates in on social media. This makes it possible to prioritize receiving relevant queries by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI execute the reception of relevant queries.

[0047] The proposal department can adjust the level of detail of a proposal based on the importance of the documents and videos used. For example, the proposal department can propose minutes of an important meeting in detail. The proposal department can propose a simplified version of a general training video. The proposal department can propose a project progress report with a moderate level of detail. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the documents and videos. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input information on the importance of documents and videos into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0048] The suggestion function can apply different suggestion algorithms depending on the category of the document or video when making suggestions. For example, the suggestion function can apply a suggestion algorithm that emphasizes technical terms to technical documents, an algorithm that emphasizes keyword frequency to marketing materials, and an algorithm that takes viewing time into account to training videos. By applying a suggestion algorithm according to the category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input category information of the document or video into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0049] The proposal department can determine the priority of proposals based on the creation dates of the materials and videos at the time of proposal. For example, the proposal department can prioritize the proposal of the latest materials and videos. The proposal department can propose older materials and videos as needed. The proposal department can adjust the priority of proposals based on the update frequency of the materials and videos. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the creation dates of the materials and videos. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information on the creation dates of materials and videos into a generating AI and have the generating AI perform the determination of proposal priorities.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the materials and videos during the proposal process. For example, the proposal unit can prioritize proposing highly relevant materials and videos. The proposal unit can postpone proposing less relevant materials and videos. The proposal unit can evaluate the relevance of materials and videos and adjust the order of proposals. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of materials and videos. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the relevance of materials and videos into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The service provider can select the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider can select the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider can prioritize providing materials and videos that the user has frequently viewed in the past. The service provider can prioritize providing display methods that the user has used in the past. The service provider can predict and provide materials and videos that the user will use at a specific time of day based on their past browsing history. This makes it possible to provide the most suitable materials and videos by referring to past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past browsing history into a generating AI and have the generating AI select the optimal service delivery method.

[0052] The service provider can customize the materials and videos provided based on the user's current work situation at the time of delivery. For example, if the user is performing project management tasks, the service provider can prioritize providing relevant materials and videos. If the user is performing technical tasks, the service provider can prioritize providing technical materials and videos. If the user is performing marketing tasks, the service provider can prioritize providing marketing-related materials and videos. This allows for more appropriate delivery by customizing materials and videos based on the current work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current work situation into a generating AI and have the generating AI perform the customization of materials and videos.

[0053] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing materials and videos related to that region. If the user is on a business trip, the service provider can prioritize providing materials and videos related to the destination of the business trip. If the user is at home, the service provider can prioritize providing materials and videos related to home. This makes it possible to provide the most suitable materials and videos by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity and suggest relevant materials and videos at the time of delivery. For example, the service provider can prioritize providing materials and videos related to topics the user frequently mentions on social media. The service provider can prioritize providing materials and videos related to accounts the user follows on social media. The service provider can prioritize providing materials and videos related to groups the user participates in on social media. This makes it possible to provide relevant materials and videos by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI suggest relevant materials and videos.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] Information retrieval systems can compare a user's past search queries with their current search queries and provide an auto-completion function for highly similar queries. For example, if a user previously searched for "project management," entering "project" in the current search query will automatically complete it with "project management." This improves the user's search efficiency.

[0057] The information retrieval system can filter search results based on the user's current work situation. For example, if a user is engaged in project management, relevant search results can be prioritized. If a user is engaged in technical work, technical search results can be prioritized. If a user is engaged in marketing work, marketing-related search results can be prioritized. This enables filtering of search results based on the user's current work situation.

[0058] Information retrieval systems can learn from a user's past search history and provide personalized search results. For example, they can prioritize relevant search results based on keywords the user has frequently searched for in the past. By analyzing a user's past search patterns, they can provide optimal search results. This makes it possible to deliver the most relevant search results to the user.

[0059] Information retrieval systems can provide highly relevant search results by considering the user's geographical location. For example, if a user is in a specific region, search results related to that region can be displayed preferentially. If a user is on a business trip, search results related to their destination can be displayed preferentially. If a user is at home, search results related to their home can be displayed preferentially. This makes it possible to provide search results that take geographical location into account.

[0060] Information retrieval systems can analyze a user's social media activity and provide relevant search results. For example, they can prioritize search results related to topics the user frequently mentions on social media, accounts the user follows on social media, and groups the user participates in on social media. This enables the provision of search results that analyze social media activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The indexing unit indexes documents and videos. For example, it analyzes the content of documents and videos and assigns keywords and tags. It can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. It can analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, it can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from the audio data. It can analyze the content of videos using image analysis and extract keywords from the image data. Step 2: The reception desk receives the user's search query. For example, it can accept search queries in the form of keyword searches or natural language searches. It can analyze keywords entered by the user and accept them as search queries. It can analyze sentences entered by the user using natural language search and accept them as search queries. Step 3: The suggestion department proposes appropriate materials and videos based on the search queries received by the reception department. It can search for and propose highly relevant materials and videos from an indexed database. It has a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. It can use generative AI to analyze the user's search intent and propose highly relevant materials and videos. It can summarize the content of materials and videos using text generation technology and propose highly relevant ones. It can learn from past search and browsing history to provide personalized suggestions. Step 4: The delivery department provides the materials and videos proposed by the proposal department to the users. These materials and videos can be provided through web applications or mobile applications.

[0063] (Example of form 2) An information retrieval system according to an embodiment of the present invention not only facilitates the search for videos and documents shared within an organization, but also automatically finds what is needed. This information retrieval system automatically indexes documents and videos within the organization and suggests appropriate items based on the user's search query. It also implements a recommendation function tailored to work content and job duties to provide users with the most suitable documents and videos. For example, the information retrieval system uses an AI agent to automatically index documents and videos within the organization. In this process, the AI ​​agent analyzes the content of the documents and videos and assigns keywords and tags. Examples include meeting minutes, presentation materials, and training videos. This makes it easier to search for documents and videos. Next, the information retrieval system uses an AI agent to suggest appropriate items in a short time based on the user's search query. When a user enters a search query, the AI ​​agent searches for highly relevant items from the indexed documents and videos. For example, for a search query such as "I want project management documents," it suggests relevant documents and videos. Furthermore, the information retrieval system implements a recommendation function tailored to work content and job duties. The AI ​​agent recommends the most suitable documents and videos based on the user's work content and job duties. For example, the system recommends project management materials to project managers and technical training videos to engineers. Furthermore, the information retrieval system utilizes generative AI to accurately understand the user's search intent. It employs large-scale language models (LLMs) to perform natural language understanding (NLU) and analyze the user's search intent. It also summarizes the content of materials and videos using text generation technology and suggests highly relevant items. For example, if a user searches for "new project plans," the generative AI summarizes and suggests relevant plans. Finally, the information retrieval system learns from past search and browsing history to provide personalized suggestions. The AI ​​agent learns from the user's past search and browsing history and suggests the most suitable materials and videos. For example, a user who has frequently searched for project management materials in the past will be suggested new and relevant materials.This allows the information retrieval system to streamline information retrieval within the organization, reduce work stress, save time, and improve productivity.

[0064] The information retrieval system according to this embodiment comprises an indexing unit, a reception unit, a proposal unit, and a provision unit. The indexing unit indexes documents and videos. The indexing unit analyzes the content of documents and videos and assigns keywords and tags. For example, the indexing unit can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. The indexing unit can also analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, the indexing unit can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from audio data. It can analyze the content of videos using image analysis and extract keywords from image data. The reception unit receives user search queries. The reception unit can receive search queries in the form of, for example, keyword search or natural language search. For example, the reception unit can analyze keywords entered by the user and accept them as search queries. The system can analyze user input using natural language search and accept it as a search query. The suggestion department proposes appropriate materials and videos based on the search queries received by the reception department. For example, the suggestion department can search for and propose highly relevant materials and videos from an indexed database. For example, the suggestion department can propose highly relevant materials and videos based on the user's search query. The suggestion department has a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. For example, the suggestion department can recommend project management materials to project managers and technical training videos to engineers. The suggestion department can analyze the user's search intent using generative AI and propose highly relevant materials and videos. For example, the suggestion department can analyze the user's search intent using generative AI and propose highly relevant materials and videos. The suggestion department can summarize the content of materials and videos using text generation technology and propose highly relevant ones.For example, the suggestion unit can summarize the content of materials and videos using text generation technology and suggest highly relevant ones. The suggestion unit can learn from past search and browsing history and make personalized suggestions. For example, the suggestion unit can learn from a user's past search and browsing history and suggest the most suitable materials and videos for the user. The provision unit provides the materials and videos suggested by the suggestion unit to the user. The provision unit can provide the materials and videos, for example, through a web application or a mobile application. For example, the provision unit can provide materials and videos through a web application. It can provide materials and videos through a mobile application. As a result, the information retrieval system according to the embodiment allows for smooth searching of materials and videos, and enables users to automatically find what they need.

[0065] The indexing unit indexes documents and videos. For example, the indexing unit analyzes the content of documents and videos and assigns keywords and tags. Specifically, it can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. This allows users to easily find related documents and videos when searching later. The indexing unit can also analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, it can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. This allows for efficient understanding of the document content and improves search accuracy. It can analyze the content of videos using audio analysis and extract keywords from the audio data. For example, it can extract important statements and topics from meeting recordings and index them as keywords. It can analyze the content of videos using image analysis and extract keywords from image data. For example, it can extract important charts and text from presentation slides or training video footage and index them as keywords. This allows the indexing unit to efficiently analyze diverse formats of materials and videos, improving search convenience. Furthermore, the indexing unit can store the analysis results in a database and utilize them in conjunction with other departments and systems. For example, the indexed data can be made accessible to the proposal and provision departments, forming a foundation for providing users with the most suitable materials and videos. In this way, the indexing unit can improve the overall performance of the information retrieval system and enhance user convenience.

[0066] The reception desk receives user search queries. The reception desk can accept search queries in various formats, such as keyword searches and natural language searches. Specifically, the reception desk can analyze keywords entered by the user and accept them as search queries. For example, if a user enters "project management," the reception desk can analyze this keyword and process it as a query to search for related documents and videos. Using natural language search, the reception desk can analyze sentences entered by the user and accept them as search queries. For example, if a user enters "I'm looking for the latest training videos on project management," the reception desk can analyze this sentence and convert it into an appropriate search query. This allows users to search using natural language, making information retrieval more intuitive. Furthermore, the reception desk also supports voice input, allowing users to enter search queries by voice. For example, if a user uses a smartphone or microphone and says "Find project management documents," the reception desk can analyze the voice data and process it as a search query. This allows users to search hands-free, improving convenience. The reception desk can learn from the user's search history and past queries to improve the accuracy of search queries. For example, for users who have frequently searched for "project management" in the past, relevant keywords and tags can be suggested preferentially. This allows the reception desk to personalize the user's search experience and provide information more efficiently.

[0067] The suggestion department proposes appropriate materials and videos based on search queries received by the reception department. For example, the suggestion department can search for and propose highly relevant materials and videos from its indexed database. Specifically, it can suggest highly relevant materials and videos based on the user's search query. For example, if a user searches for materials related to "project management," the suggestion department can prioritize suggesting project management-related materials and videos from its indexed database. The suggestion department also features a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. For example, the suggestion department can recommend project management materials to project managers and technical training videos to engineers. This allows users to efficiently obtain the most relevant information for their work. The suggestion department can also utilize generative AI to analyze the user's search intent and propose highly relevant materials and videos. For example, the suggestion department can use generative AI to analyze the user's search intent and propose highly relevant materials and videos. The generative AI analyzes the user's search query, extracts relevant keywords and tags, and proposes the most suitable materials and videos. The suggestion function can summarize the content of documents and videos using text generation technology and suggest highly relevant ones. For example, the suggestion function can summarize the content of documents and videos using text generation technology and suggest highly relevant ones. This allows users to grasp the necessary information in a short amount of time. The suggestion function can learn from past search and browsing history and provide personalized suggestions. For example, the suggestion function can learn from a user's past search and browsing history and suggest the most suitable documents and videos for that user. This allows the suggestion function to provide optimal information tailored to the user's needs and improve the search experience.

[0068] The provisioning department provides users with materials and videos proposed by the proposal department. The provisioning department can provide materials and videos, for example, through web applications or mobile applications. Specifically, the provisioning department can provide materials and videos through web applications. For example, when a user accesses the information retrieval system using a web browser, the provisioning department can display the proposed materials and videos on a web page. Users can view materials and play videos on the web page. Materials and videos can also be provided through mobile applications. For example, when a user accesses the information retrieval system using a smartphone or tablet, the provisioning department can display the proposed materials and videos on the mobile application. Users can view materials and play videos on the mobile application. This allows users to access the information they need, regardless of location or time. Furthermore, the provisioning department can provide a download function for materials and videos. For example, it can allow users to download proposed materials and view them offline. The provisioning department can also provide a sharing function for materials and videos. For example, it can allow users to share proposed materials and videos with other users. This allows users to efficiently obtain the information they need and share it with other users. The service provider can collect user feedback and continuously improve the quality of the materials and videos they provide. For example, users can leave ratings and comments on the materials and videos provided. This allows the service provider to provide the most suitable information to meet user needs and improve the overall quality of the information retrieval system.

[0069] The indexing unit can analyze the content of documents and videos and assign keywords and tags. For example, the indexing unit can analyze the content of documents and videos and assign keywords and tags. For example, the indexing unit can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. In addition, the indexing unit can analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, the indexing unit can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from the audio data. It can analyze the content of videos using image analysis and extract keywords from the image data. This makes it easier to search for documents and videos.

[0070] The recommendation section features a recommendation function that suggests the most suitable materials and videos based on the user's work content and job responsibilities. For example, the recommendation section can recommend project management materials to project managers and technical training videos to engineers. The recommendation section can perform recommendations using algorithms such as collaborative filtering and content-based filtering. For example, collaborative filtering can be used to make recommendations based on materials and videos viewed by other users. Content-based filtering can be used to make recommendations based on the user's past browsing and search history. This allows the recommendation section to provide the user with the most suitable materials and videos.

[0071] The proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. For example, the proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. For example, the proposal department can use generative AI to analyze the user's search intent and suggest highly relevant materials and videos. Generative AI is implemented using models such as large-scale language models. Generative AI takes the user's search query as input and outputs highly relevant materials and videos. For example, generative AI takes the user's search query as input and outputs highly relevant materials and videos. This makes it possible to suggest appropriate materials and videos based on the user's search intent.

[0072] The proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. For example, the proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. For example, the proposal department can summarize the content of documents and videos using text generation technology and propose highly relevant items. Text generation technology can be implemented using technologies such as natural language generation or template-based generation. Text generation technology takes the content of documents and videos as input and outputs a summary. For example, text generation technology takes the content of documents and videos as input and outputs a summary. This allows users to quickly obtain the information they need by summarizing and proposing the content of documents and videos.

[0073] The suggestion unit can learn from past search and browsing history to provide personalized suggestions. For example, the suggestion unit can learn from past search and browsing history to provide personalized suggestions. For example, the suggestion unit can learn from a user's past search and browsing history to suggest the most suitable materials and videos for the user. The suggestion unit can use technologies such as user preference analysis and behavioral pattern analysis to provide personalized suggestions. For example, the suggestion unit can analyze user preferences to suggest the most suitable materials and videos for the user. The suggestion unit can analyze user behavior patterns to suggest the most suitable materials and videos for the user. This allows the suggestion unit to provide the most suitable materials and videos for the user.

[0074] The indexing unit can estimate the user's emotions and adjust the indexing priority based on the estimated emotions. For example, if the user is stressed, the indexing unit can prioritize indexing high-priority materials and videos. If the user is relaxed, the indexing unit can perform a well-balanced indexing overall. If the user is in a hurry, the indexing unit can adjust the indexing priority to provide the necessary information in a short amount of time. This allows for more appropriate indexing by adjusting the indexing priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input user emotion data into the generating AI and have the generating AI adjust the indexing priority.

[0075] The indexing unit can adjust the level of detail in indexing documents and videos based on the importance of the content. For example, the indexing unit can index minutes of important meetings in detail to make them easier to search. The indexing unit can perform simplified indexing on general training videos. The indexing unit can index project progress reports with a moderate level of detail. This improves search accuracy by adjusting the level of detail in indexing based on the importance of the content. Some or all of the above processing in the indexing unit may be performed using AI, for example, or not using AI. For example, the indexing unit can input the content of documents and videos into a generating AI and have the generating AI perform the adjustment of the level of detail in indexing.

[0076] The indexing unit can apply different indexing algorithms depending on the category of the document or video during the indexing process. For example, the indexing unit can apply an indexing algorithm that emphasizes technical terms to technical documents, an indexing algorithm that emphasizes keyword frequency to marketing documents, and an indexing algorithm that takes viewing time into account to training videos. By applying a category-appropriate indexing algorithm, the accuracy of searches is improved. Some or all of the above-described processes in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input category information of documents and videos into a generating AI and have the generating AI execute the application of the indexing algorithm.

[0077] The indexing unit can estimate the user's emotions and adjust the indexing method based on the estimated emotions. For example, if the user is stressed, the indexing unit can adopt a simple indexing method. If the user is relaxed, the indexing unit can adopt a detailed indexing method. If the user is in a hurry, the indexing unit can adopt a method for rapid indexing. By adjusting the indexing method according to the user's emotions, more appropriate indexing becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the indexing method.

[0078] The indexing unit can perform indexing while considering the attribute information of the creators of the materials and videos. For example, if the creator of the materials and videos is an expert, the indexing unit can perform indexing that emphasizes specialized terminology. If the creator of the materials and videos is a beginner, the indexing unit can perform indexing that emphasizes general terminology. If the creator of the materials and videos belongs to a specific industry, the indexing unit can perform indexing that emphasizes industry-specific terminology. This makes it possible to perform more appropriate indexing by considering the attribute information of the creators. Some or all of the above processing in the indexing unit may be performed using AI, for example, or without using AI. For example, the indexing unit can input the attribute information of the creators of the materials and videos into a generating AI and have the generating AI perform the indexing.

[0079] The indexing unit can improve the accuracy of indexing by referring to related literature for documents and videos during the indexing process. For example, the indexing unit can refer to related literature for documents and videos to improve the accuracy of indexing. For example, the indexing unit can refer to related literature for documents and videos to select keywords. The indexing unit can refer to related literature for documents and videos to assign tags. The indexing unit can refer to related literature for documents and videos to adjust the level of detail of the indexing. As a result, the accuracy of indexing is improved by referring to related literature. Some or all of the above processes in the indexing unit may be performed using AI, for example, or without AI. For example, the indexing unit can input related literature for documents and videos into a generating AI and have the generating AI perform the indexing accuracy improvement.

[0080] The reception unit can estimate the user's emotions and adjust how it receives search queries based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input and quickly receive the search query. This allows for more appropriate search query reception by adjusting how it receives search queries according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​adjust how search queries are received.

[0081] The reception unit can select the optimal reception method when receiving a search query by referring to the user's past search history. For example, the reception unit can automatically display search queries that the user has frequently entered in the past as suggestions. The reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can predict and suggest search queries to be used during a specific time period based on the user's past search history. This makes it possible to receive the optimal search query by referring to past search history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past search history into a generating AI and have the generating AI select the optimal reception method.

[0082] The reception unit can filter search queries based on the user's current work situation. For example, if the user is performing project management tasks, the reception unit can prioritize receiving relevant search queries. If the user is performing technical tasks, the reception unit can prioritize receiving technical search queries. If the user is performing marketing tasks, the reception unit can prioritize receiving marketing-related search queries. This allows for the reception of more appropriate search queries by filtering based on the user's current work situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current work situation into a generating AI and have the generating AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and prioritize search queries based on those emotions. For example, if the user is stressed, the reception desk can prioritize high-priority search queries. If the user is relaxed, the reception desk can process a balanced mix of search queries. If the user is in a hurry, the reception desk can prioritize search queries that can be processed quickly. This allows for more appropriate processing of search queries by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of search queries.

[0084] The reception unit can prioritize receiving highly relevant queries by considering the user's geographical location when receiving search queries. For example, the reception unit can prioritize receiving highly relevant queries by considering the user's geographical location when receiving search queries. For example, if the user is in a specific region, the reception unit can prioritize receiving search queries related to that region. If the user is on a business trip, the reception unit can prioritize receiving search queries related to the business trip destination. If the user is at home, the reception unit can prioritize receiving search queries related to home. In this way, it becomes possible to prioritize receiving highly relevant queries by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI execute the process of receiving highly relevant queries.

[0085] The reception unit can analyze the user's social media activity when receiving search queries and accept relevant queries. For example, the reception unit can prioritize receiving search queries related to topics that the user frequently mentions on social media. The reception unit can prioritize receiving search queries related to accounts that the user follows on social media. The reception unit can prioritize receiving search queries related to groups that the user participates in on social media. This makes it possible to prioritize receiving relevant queries by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI execute the reception of relevant queries.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0087] The proposal department can adjust the level of detail of a proposal based on the importance of the documents and videos used. For example, the proposal department can propose minutes of an important meeting in detail. The proposal department can propose a simplified version of a general training video. The proposal department can propose a project progress report with a moderate level of detail. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the documents and videos. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input information on the importance of documents and videos into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0088] The suggestion function can apply different suggestion algorithms depending on the category of the document or video when making suggestions. For example, the suggestion function can apply a suggestion algorithm that emphasizes technical terms to technical documents, an algorithm that emphasizes keyword frequency to marketing materials, and an algorithm that takes viewing time into account to training videos. By applying a suggestion algorithm according to the category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input category information of the document or video into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. If the user is in a hurry, the suggestion unit can make a short, easily understandable suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0090] The proposal department can determine the priority of proposals based on the creation dates of the materials and videos at the time of proposal. For example, the proposal department can prioritize the proposal of the latest materials and videos. The proposal department can propose older materials and videos as needed. The proposal department can adjust the priority of proposals based on the update frequency of the materials and videos. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the creation dates of the materials and videos. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information on the creation dates of materials and videos into a generating AI and have the generating AI perform the determination of proposal priorities.

[0091] The proposal unit can adjust the order of proposals based on the relevance of the materials and videos during the proposal process. For example, the proposal unit can prioritize proposing highly relevant materials and videos. The proposal unit can postpone proposing less relevant materials and videos. The proposal unit can evaluate the relevance of materials and videos and adjust the order of proposals. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of materials and videos. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information on the relevance of materials and videos into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0092] The service provider can estimate the user's emotions and adjust the display method of the materials and videos provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate materials and videos. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0093] The service provider can select the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider can select the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider can prioritize providing materials and videos that the user has frequently viewed in the past. The service provider can prioritize providing display methods that the user has used in the past. The service provider can predict and provide materials and videos that the user will use at a specific time of day based on their past browsing history. This makes it possible to provide the most suitable materials and videos by referring to past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past browsing history into a generating AI and have the generating AI select the optimal service delivery method.

[0094] The service provider can customize the materials and videos provided based on the user's current work situation at the time of delivery. For example, if the user is performing project management tasks, the service provider can prioritize providing relevant materials and videos. If the user is performing technical tasks, the service provider can prioritize providing technical materials and videos. If the user is performing marketing tasks, the service provider can prioritize providing marketing-related materials and videos. This allows for more appropriate delivery by customizing materials and videos based on the current work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current work situation into a generating AI and have the generating AI perform the customization of materials and videos.

[0095] The service provider can estimate the user's emotions and prioritize the materials and videos it provides based on those emotions. For example, if the user is stressed, the service provider can prioritize providing high-priority materials and videos. If the user is relaxed, the service provider can provide materials and videos that are well-balanced overall. If the user is in a hurry, the service provider can prioritize providing materials and videos that can be delivered quickly. This allows for the provision of more appropriate materials and videos by prioritizing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0096] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing materials and videos related to that region. If the user is on a business trip, the service provider can prioritize providing materials and videos related to the destination of the business trip. If the user is at home, the service provider can prioritize providing materials and videos related to home. This makes it possible to provide the most suitable materials and videos by taking geographical location information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0097] The service provider can analyze the user's social media activity and suggest relevant materials and videos at the time of delivery. For example, the service provider can prioritize providing materials and videos related to topics the user frequently mentions on social media. The service provider can prioritize providing materials and videos related to accounts the user follows on social media. The service provider can prioritize providing materials and videos related to groups the user participates in on social media. This makes it possible to provide relevant materials and videos by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI suggest relevant materials and videos.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] Information retrieval systems can estimate a user's emotions and adjust the display order of search results based on those emotions. For example, if a user is stressed, the most relevant results can be prioritized. If a user is relaxed, a wide range of options can be offered. If a user is in a hurry, results that can be accessed quickly can be displayed at the top. This makes it possible to display search results that are tailored to the user's emotions.

[0100] Information retrieval systems can compare a user's past search queries with their current search queries and provide an auto-completion function for highly similar queries. For example, if a user previously searched for "project management," entering "project" in the current search query will automatically complete it with "project management." This improves the user's search efficiency.

[0101] Information retrieval systems can estimate a user's emotions and adjust the display format of search results based on those emotions. For example, if a user is stressed, a simple and highly visible display format can be provided. If a user is relaxed, a display format containing detailed information can be provided. If a user is in a hurry, a display format that gets straight to the point can be provided. This allows for adjustment of the display format according to the user's emotions.

[0102] The information retrieval system can filter search results based on the user's current work situation. For example, if a user is engaged in project management, relevant search results can be prioritized. If a user is engaged in technical work, technical search results can be prioritized. If a user is engaged in marketing work, marketing-related search results can be prioritized. This enables filtering of search results based on the user's current work situation.

[0103] Information retrieval systems can estimate a user's emotions and provide a summary of search results based on those emotions. For example, if a user is stressed, a short, to-the-point summary can be provided. If a user is relaxed, a detailed summary can be provided. If a user is in a hurry, a summary that can be quickly understood can be provided. This makes it possible to provide summaries tailored to the user's emotions.

[0104] Information retrieval systems can learn from a user's past search history and provide personalized search results. For example, they can prioritize relevant search results based on keywords the user has frequently searched for in the past. By analyzing a user's past search patterns, they can provide optimal search results. This makes it possible to deliver the most relevant search results to the user.

[0105] Information retrieval systems can estimate a user's emotions and adjust how search queries are processed based on those emotions. For example, if a user is stressed, a simple interface can be provided, minimizing the input steps. If a user is relaxed, detailed input options can be offered, and customizable input methods can be suggested. If a user is in a hurry, voice input can be prioritized, allowing for rapid processing of search queries. This enables the processing of search queries to be adjusted according to the user's emotions.

[0106] Information retrieval systems can provide highly relevant search results by considering the user's geographical location. For example, if a user is in a specific region, search results related to that region can be displayed preferentially. If a user is on a business trip, search results related to their destination can be displayed preferentially. If a user is at home, search results related to their home can be displayed preferentially. This makes it possible to provide search results that take geographical location into account.

[0107] Information retrieval systems can analyze a user's social media activity and provide relevant search results. For example, they can prioritize search results related to topics the user frequently mentions on social media, accounts the user follows on social media, and groups the user participates in on social media. This enables the provision of search results that analyze social media activity.

[0108] Information retrieval systems can estimate a user's emotions and adjust how search results are displayed based on those estimates. For example, if a user is stressed, a simple and highly visible display can be provided. If a user is relaxed, a display with detailed information can be provided. If a user is in a hurry, a display that focuses on the essentials can be provided. This allows for adjustments to the display method according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The indexing unit indexes documents and videos. For example, it analyzes the content of documents and videos and assigns keywords and tags. It can analyze meeting minutes, presentation materials, training videos, etc., and assign keywords and tags. It can analyze the content of documents and videos using technologies such as text analysis, audio analysis, and image analysis. For example, it can analyze the content of documents using text analysis, extract frequently occurring words, and assign keywords. It can analyze the content of videos using audio analysis and extract keywords from the audio data. It can analyze the content of videos using image analysis and extract keywords from the image data. Step 2: The reception desk receives the user's search query. For example, it can accept search queries in the form of keyword searches or natural language searches. It can analyze keywords entered by the user and accept them as search queries. It can analyze sentences entered by the user using natural language search and accept them as search queries. Step 3: The suggestion department proposes appropriate materials and videos based on the search queries received by the reception department. It can search for and propose highly relevant materials and videos from an indexed database. It has a recommendation function that recommends the most suitable materials and videos based on the user's work content and job responsibilities. It can use generative AI to analyze the user's search intent and propose highly relevant materials and videos. It can summarize the content of materials and videos using text generation technology and propose highly relevant ones. It can learn from past search and browsing history to provide personalized suggestions. Step 4: The delivery department provides the materials and videos proposed by the proposal department to the users. These materials and videos can be provided through web applications or mobile applications.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the indexing unit, reception unit, suggestion unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the indexing unit is implemented by the processor 46 of the smart device 14, which analyzes the content of documents and videos and assigns keywords and tags. The reception unit receives user search queries using, for example, the touch panel 38A or microphone 38B of the smart device 14. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which searches for highly relevant items from the indexed documents and videos and suggests them. The provision unit provides documents and videos to the user through, for example, the display 40A or speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the indexing unit, reception unit, suggestion unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the indexing unit is implemented by the processor 46 of the smart glasses 214, which analyzes the content of materials and videos and assigns keywords and tags. The reception unit receives user search queries using the microphone 238 of the smart glasses 214. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for highly relevant materials and videos from the indexed materials and videos and suggests them. The provision unit provides materials and videos to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the indexing unit, reception unit, suggestion unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the indexing unit is implemented by the processor 46 of the headset terminal 314, which analyzes the content of materials and videos and assigns keywords and tags. The reception unit receives user search queries using the microphone 238 of the headset terminal 314. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for highly relevant materials and videos from the indexed materials and videos and suggests them. The provision unit provides materials and videos to the user through the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the indexing unit, reception unit, suggestion unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the indexing unit is implemented by the processor 46 of the robot 414, which analyzes the content of documents and videos and assigns keywords and tags. The reception unit receives user search queries using the microphone 238 of the robot 414. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for highly relevant items from the indexed documents and videos and suggests them. The provision unit provides documents and videos to the user through the speaker 240 of the robot 414 or the controlled object 443. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An indexing unit that indexes documents and videos, A reception desk that receives user search queries, Based on the search queries received by the aforementioned reception department, the proposal department proposes appropriate materials and videos. The system includes a provisioning unit that provides users with materials and videos proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned indexing unit, Analyze the content of documents and videos and assign keywords and tags. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It features a recommendation function that suggests the most suitable materials and videos based on the user's work content and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We utilize generative AI to analyze users' search intent and suggest highly relevant materials and videos. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We use text generation technology to summarize the content of documents and videos and suggest the most relevant ones. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It learns from your past search and browsing history to provide personalized suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned indexing unit, It estimates user sentiment and adjusts indexing priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned indexing unit, When indexing documents and videos, adjust the level of detail in the index based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned indexing unit, When indexing, different indexing algorithms are applied depending on the category of the document or video. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned indexing unit, We estimate user sentiment and adjust the indexing method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned indexing unit, When indexing, the attribute information of the creators of the documents and videos will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned indexing unit, During the indexing process, we improve the accuracy of the indexing by referring to related literature for documents and videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's sentiment and adjusts how search queries are accepted based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving a search query, the system selects the optimal processing method by referring to the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When a search query is received, it is filtered based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is It estimates user sentiment and prioritizes search queries based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is When receiving search queries, the system prioritizes accepting queries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is When receiving a search query, the system analyzes the user's social media activity and accepts relevant queries. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the materials and videos. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the document or video. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of creating documents and videos. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the materials and videos. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how materials and videos are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, the system will refer to the user's past browsing history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the materials and videos offered will be customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the materials and videos provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will analyze the user's social media activity and suggest materials and videos to be included. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An indexing unit that indexes documents and videos, A reception desk that receives user search queries, Based on the search queries received by the aforementioned reception department, the proposal department proposes appropriate materials and videos. The system includes a provisioning unit that provides users with materials and videos proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned indexing unit, Analyze the content of documents and videos and assign keywords and tags. The system according to feature 1.

3. The aforementioned proposal section is, It features a recommendation function that suggests the most suitable materials and videos based on the user's work content and job responsibilities. The system according to feature 1.

4. The aforementioned proposal section is, By utilizing generative AI, we analyze the user's search intent and suggest highly relevant materials and videos. The system according to feature 1.

5. The aforementioned proposal section is, We use text generation technology to summarize the content of documents and videos and suggest the most relevant ones. The system according to feature 1.

6. The aforementioned proposal section is, It learns from your past search and browsing history to provide personalized suggestions. The system according to feature 1.

7. The aforementioned indexing unit, It estimates user sentiment and adjusts indexing priorities based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned indexing unit, When indexing documents and videos, adjust the level of detail in the index based on the importance of the content. The system according to feature 1.

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